System and method for implementing personal emergency response system based on uwb interferometer

ABSTRACT

A non-wearable Personal Emergency Response System (PERS) architecture is provided, having a synthetic aperture antenna based RF interferometer followed by two-stage human state classifier and abnormal states pattern recognition. In addition, it contains a communication sub-system to communicate with the remote operator and centralized system for multiple users&#39; data analysis. The system is trained to learn the person&#39;s body features as well as the home environment. The decision process is carried out based on the instantaneous human state (Local Decision) followed by abnormal states patterns recognition (Global decision). The system global decision (emergency alert) is communicated to the operator through the communication system and two-ways communication is enabled between the monitored person and the remote operator. In some embodiments, a centralized system (cloud) receives data from distributed PERS systems to perform further analysis and upgrading the systems with updated database (codebooks).

FIELD OF THE INVENTION

The present invention relates to the field of elderly monitoring, andmore particularly, to a system architecture for personal emergencyresponse system (PERS).

BACKGROUND OF THE INVENTION

Elderly people have a higher risk of falling, for example, inresidential environments. As most of elder people will need immediatehelp after such a fall, it is crucial that these falls are monitored andaddressed upon in real time. Specifically, one fifth of falling eldersare admitted to hospital after staying on floor for over one hourfollowing a fall. The late admission increases the risk of dehydration,pressure ulcers, hypothermia, and pneumonia. Acute falls leads to highpsychological effect of fear and high impact on daily life quality.

Most of the existing personal emergency response systems (PERS), whichtake the form of fall detectors and alarm buttons, are wearable devices.These wearable devices have several disadvantages. First, they cannotrecognize the human body positioning and posture.

Second, they suffer from limited acceptance and use due to: Elders'perception and image issues, high rate of false alarms and miss-detects,elders neglect re-wearing when getting out of bed or bath, and, and longterm usage of wearable might lead to user skin irritations. Third, thewearable PERS are used mainly after experiencing a fall (very limitedaddressable market).

Therefore, there is a need for a paradigm shift toward automated andremote monitoring systems.

SUMMARY OF THE INVENTION

Some embodiments of the present invention provide a unique sensingsystem and a breakthrough for the supervision of the elderly duringtheir stay in the house, in general, and detect falls, in particular.The system may include: a UWB-RF Interferometer, Vector Quantizationbased Human states classifier, Cognitive situation analysis,communication unit and processing unit.

According to some embodiments of the present invention, the system maybe installed in the house's ceiling, and covers a typical elder'sapartment with a single sensor, using Ultra-Wideband RF technology. Itis a machine learning based solution that learns the elder's uniquecharacteristics (e.g., stature, gait and the like) and home primarylocations (e.g. bedroom, restroom, bathroom, kitchen, entry, etc.), aswell as the home external walls boundaries.

According to some embodiments of the present invention, the system mayautomatically detect and alert emergency situation that might beencountered by elders while being at home and identify the emergencysituations.

According to some embodiments of the present invention, the system maydetect falls of elderly people, but may also identify other emergenciessituations, such as labor briefing, sleep apnea, as well as otherabnormal cases, e.g., sedentary situation, repetitive non-acute fallsthat are not reported by the person. It is considered as a key elementfor the elderly connected smart home, and, by connecting the system tothe network and cloud, it can also make a use of big data analytics toidentify new patterns of emergencies and abnormal situations.

These, additional, and/or other aspects and/or advantages of the presentinvention are set forth in the detailed description which follows;possibly inferable from the detailed description; and/or learnable bypractice of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter regarded as the invention is particularly pointed outand distinctly claimed in the concluding portion of the specification.The invention, however, both as to organization and method of operation,together with objects, features, and advantages thereof, may best beunderstood by reference to the following detailed description when readwith the accompanying drawings in which:

FIG. 1 is a block diagram illustrating a non-limiting exemplaryarchitecture of a system in accordance with embodiments of the presentinvention

FIG. 2 is another block diagram illustrating the architecture of asystem in further details in accordance with embodiments of the presentinvention;

FIG. 3 is a diagram illustrating conceptual 2D Synthetic ApertureAntennas arrays in accordance with some embodiments of the presentinvention;

FIG. 4 is a table illustrating an exemplary states definition inaccordance with some embodiments of the present invention;

FIG. 5 is a table illustrating an exemplary states matrix in accordancewith some embodiments of the present invention;

FIG. 6 is a table illustrating an exemplary abnormal patterns inaccordance with some embodiments of the present invention; and

FIG. 7 is a diagram illustrating a cloud based architecture of thesystem in accordance with embodiments of the present invention;

FIG. 8 is a floor plan diagram illustrating initial monitored persontraining as well as the home environment and primary locations trainingin accordance with embodiments of the present invention; and

FIG. 9 is a diagram illustrating yet another aspect in accordance withsome embodiments of the present invention.

It will be appreciated that for simplicity and clarity of illustration,elements shown in the figures have not necessarily been drawn to scale.For example, the dimensions of some of the elements may be exaggeratedrelative to other elements for clarity. Further, where consideredappropriate, reference numerals may be repeated among the figures toindicate corresponding or analogous elements.

DETAILED DESCRIPTION OF THE INVENTION

With specific reference now to the drawings in detail, it is stressedthat the particulars shown are by way of example and for purposes ofillustrative discussion of the preferred embodiments of the presentinvention only, and are presented in the cause of providing what isbelieved to be the most useful and readily understood description of theprinciples and conceptual aspects of the invention. In this regard, noattempt is made to show structural details of the invention in moredetail than is necessary for a fundamental understanding of theinvention, the description taken with the drawings making apparent tothose skilled in the art how the several forms of the invention may beembodied in practice.

Before at least one embodiment of the invention is explained in detail,it is to be understood that the invention is not limited in itsapplication to the details of construction and the arrangement of thecomponents set forth in the following description or illustrated in thedrawings. The invention is applicable to other embodiments or of beingpracticed or carried out in various ways. Also, it is to be understoodthat the phraseology and terminology employed herein is for the purposeof description and should not be regarded as limiting.

FIG. 1 is a block diagram illustrating a non-limiting exemplaryarchitecture of a system 100 in accordance with embodiments of thepresent invention. System 100 may include a radio frequency (RF)interferometer 120 configured to transmit signals via Tx antenna 101 andreceive echo signals via array 110-1 to 110-N. It should be noted thattransmit antennas and receive antennas may take different form andaccording to a preferred embodiment, in each antenna array they may be asingle transmit antenna and several receive antennas. An environmentalclutter cancellation module may or may not be used to filter out staticnon-human related echo signals. System 100 may include a human statefeature extractor 130 configured to extract from the filtered echosignals, a quantified representation of position postures, movements,motions and breathing of at least one human located within the specifiedarea. A human state classifier may be configured to identify a mostprobable fit of human current state that represents an actual humaninstantaneous status. System 100 may include a abnormality situationpattern recognition module 140 configured to apply a pattern recognitionbased decision function to the identified states patterns and determinewhether an abnormal physical event has occurred to the at least onehuman in the specified area. A communication system 150 forcommunicating with a remote server and end-user equipment for alerting(not shown here). Communication system 150 may further include two-waycommunication system between the caregiver and the monitored person forreal-time assistance.

FIG. 2 is another block diagram illustrating the architecture of asystem in further details in accordance with some embodiments of thepresent invention as follows:

UWB-RF interferometer 220—this unit transmits an Ultra wideband signal(e.g., pulse) into the monitored environment and receives back the echosignals from multiple antenna arrays to provide a better spatialresolution by using the Synthetic Antenna Aperture approach. In order toincrease the received signal-to-noise (SNR), the transmitter sendsmultiple UWB pulse and receiver receives and integrates multiple echosignals (processing gain). The multiple received signals (one signal pereach Rx Antenna) are sampled and digitally stored for further signalprocessing.

Environmental Clutter Cancellation 230. The echo signals arepre-processed to reduce the environmental clutter (the unwantedreflected echo components that are arrived from the home walls,furniture, etc.). The output signal mostly contains only the echocomponents that reflected back from the monitored human body.Environmental Clutter Cancellation 230 is fed with the trainedenvironmental parameters 232. In addition, the clutter cancellationincludes a stationary environment detection (i.e., no human body atzone) to retrain the reference environmental clutter for doors orfurniture movement cases.

Feature extraction 240—The “cleaned” echo signals are then processed toextract the set of features that will be used to classify theinstantaneous state of the monitored human person (e.g. posture,location, motion, movement, breathing). The set of the extractedfeatures constructs the feature vector that is the input for theclassifier.

Human state classifier 250—The features vector is entered to a VectorQuantization based classifier that classifies the instantaneous featuresvector by statistically finding the closest pre-trained state out of aset of N possible states, i.e., finding the closest code vector(centroid) out of all code vectors in a codebook 234. The classifieroutput is the most probable states with its relative probability (localdecision).

Cognitive Situation Analysis (CSA) 260—This unit recognizes whether themonitored person is in an emergency or abnormal situation. This unit isbased on a pattern recognition engine (e.g., Hidden Markov Model—HMM,based). The instantaneous states with their probabilities are streamedin and the CSA search for states patterns that are tagged as emergencyor abnormal patterns, such as a fall. These predefined patterns arestored in a patterns codebook 234. If case that CSA recognizes such apattern, it will send an alarm notification to the healthcare center orfamily care giver through the communication unit (e.g., Wi-Fi orcellular).

Two-way voice/video communication unit 150—this unit may be activated bythe remote caregiver to communicate with the monitored person whennecessary.

The UWB-RF interferometer unit 220 may include the following blocks:

Two-dimensional UWB antenna array 110-1-110-N to generate the syntheticaperture through all directions, followed by antenna selector.

UWB pulse generator and Tx RF chain to transmit the pulse to themonitored environment

UWB Rx chain to receive the echo signals from the antenna array followedby analog to digital converter (ADC).

The sampled signals (from each antenna) are stored in the memory, suchas SRAM or DRAM.

In order to increase the received SNR, the RF interferometer repeats thepulse transmission and echo signal reception per each antenna (of theantenna array) and coherently integrates the digital signal to improvethe SNR.

Environmental Clutter Cancellation

The environmental clutter cancellation is required to remove theunwanted echo components that are reflected from the apartment's staticitems as walls, doors, furniture, etc.

The clutter cancellation is done by subtracting the unwantedenvironmental clutter from the received echo signals. The residualclutter represents the reflected echo signals from the monitored humanbody.

According to some embodiments of the present invention, the cluttercancellation also includes stationary environment detection to detect ifno person at the environment, such as when the person is not at home, oris not at the estimated zone. Therefore, a periodic stationary cluttercheck is carried out and new reference clutter fingerprint is capturedwhen the environment is identified as stationary.

The system according to some embodiments of the present inventionre-estimates the environmental clutter to overcome the clutter changesdue to doors or furniture movements.

Multiple Features Extraction

The “cleaned” echo signal vectors are used as the raw data for thefeatures extraction unit. This unit extracts the features that mostlydescribe the instantaneous state of the monitored person. The followingare examples for the set of the extracted features and the method it'sextracted:

Position—the position is extracted as the position (in case of2D—angle/range, in case of 3D—x,y,z coordinates) metrics output of eacharray baseline. The actual person position at home will be determined asa “finger print” method, i.e., the most proximity to the pre-trainedhome position matrices (centroids) codebook.

Posture—the person posture (sitting, standing, and laying) will beextracted by creating the person “image” by using, e.g., aback-projection algorithm.

Both position and posture are extracted, for example, by operating,e.g., the Back-projection algorithm on received echo signals—as acquiredfrom the multiple antennas array in SAR operational mode.

The following is the used procedure to find the human position andposture:

Dividing the surveillance space into voxels (small cubic) in crossrange, down range and height

Estimating the reflected EM signal from a specific voxel by the backprojection algorithm

Estimating the human position by averaging the coordinates of the humanreflecting voxels for each baseline (Synthetic Aperture Antenna Array).

Triangulating all baselines' position to generate the human position inthe environment

Estimating the human posture by mapping the human related high-powervoxels into the form-factor vector

Tracking the human movements in the environment (bedroom, restroom,etc.)

Human motion—The monitored human body may create vibrations and othermotions (as gestures and gait). Therefore, it introduces frequencymodulation on the returned echo signal. The modulation due to thesemotions is referred to as micro-Doppler (m-D) phenomena. The humanbody's motion feature is extracted by estimating the micro-Dopplerfrequency shift vector at the target distance from the system (downrange).

Human breathing—During the breathing (respiration) the chest wall moves.The average respiratory rate of a healthy adult is usually 12-20breaths/min at rest (˜0.3 Hz) and 35-45 breaths/min (˜0.75 Hz) duringlabored breathing. The breathing frequency feature is extracted byestimating the spectrum on the slow-time sampled received echo signal atthe target distance (down range) from the system.

The features vector is prepared by quantizing the extracted featureswith a final number of bits per field and adding the time stamp for theprepared vector. This vector is used as the entry data for the humanstate classifier (for both training and classifying stages).

Human State Classifier

The Human state classifier is a VQ (Vector Quantization) basedclassifier. This classifier consists of two main phases:

Training phase—it's done offline (supervised training) and online(unsupervised training), where a stream of features vectors reflectingvarious states are used as a preliminary database for vectorquantization and finding the set of code-vectors (centroids) thatsufficiently representing the instantaneous human states. The set of thecalculated code-vectors are called codebook. Some embodiments of thetraining sessions are provided in more details hereinafter.

Classifying phase—it's executed during the online operation while anunknown features vector is entered into the classifier and theclassifier determines what the most probable state that it represents.The classifier output is the determined states and the set of themeasured statistical distances (probabilities), i.e., the probability ofState-i given the observation-O (the features vector). Theaforementioned probability scheme may be formulated by: P (Si|O). Thedetermined instantaneous state is called “Local Decision”.

The VQ states are defined as the set of instantaneous states at variouslocations at the monitored home environment. Therefore, any state is a 2dimension results which is mapped on the VQ state matrix.

The State matrix consists of the state (row) and location (Column)followed by a time stamp. Typical elderly home environment consists ofthe specific locations (Primary zones) and others non-specifiedlocations (Secondary zones). State is defined as the combination ofposture/motion at a specific location (e.g. S²¹ will indicate sleepingat Bedroom).

FIG. 3 is a diagram illustrating conceptual 2D Synthetic ApertureAntennas arrays in accordance with some embodiments of the presentinvention. Antenna array system 300 may include several arrays ofantennas 320, 330, 340, and 350. Each row may have at least one transmitantenna and a plurality of receive antennas. The aforementionednon-limiting exemplary configuration enables to validate a location of areal target 310 by eliminating the possible images 310A and 310B afterchecking reflections received at corresponding arrays of antennas 330and 320, respectively. It is well understood that the aforementionedconfiguration is a non-limiting example and other antennasconfigurations may be used effectively.

FIG. 4 is a table 400 illustrating an exemplary states definition inaccordance with some embodiments of the present invention.

FIG. 5 is a table 500 illustrating an exemplary states matrix inaccordance with some embodiments of the present invention.

Cognitive Situation Analysis (CSA)

The CSA's objective is to recognize the abnormal human patternsaccording to a trained model that contains the possible abnormal cases(e.g., fall). The core of the CSA, in this embodiment, may, in anon-limiting example a Hidden Markov Model (HMM) based patternrecognition.

The CSA engine searches for states patterns that are tagged as anemergencies or abnormal patterns. These predefined patterns are storedin a patterns codebook.

The output of the CSA is the Global recognized human situation.

FIG. 6 is a table 600 illustrating exemplary abnormal patterns inaccordance with some embodiments of the present invention. It can beseen that in the first abnormal case (Critical fall), it appears thatthe person was sleeping in the leaving room (S25), then was standing(S45) and immediately fell down (S65). He stayed on floor (S15) andstart being in stress due to high respiration rate (S75).

The CSA may contain additional codebook (irrelevant codebook) toidentify irrelevant patterns that might mislead the system decision.

Communication Unit

The communication unit creates the channel between the system and theremote caregiver (family member or operator center). It may be based oneither wired (Ethernet) connectivity or wireless (e.g., cellular or WiFicommunication or any other communication channel).

The communication unit provides the following functionalities:

-   -   1. This unit transmits any required ongoing situation of the        monitored person and emergency alerts.    -   2. It enables the two way voice/video communication with the        monitored person when necessary. Such a communication is        activated either automatically whenever the system recognizes an        emergency situation or remotely by the caregiver.    -   3. It enables the remote system upgrades for both software and        updated codebooks (as will be in further detail below)    -   4. It enables the communication to the centralized system        (cloud) to share common information and for further big data        analytics based on multiple deployments of such innovated system

FIG. 7 is a diagram illustrating cloud-based architecture 700 of thesystem in accordance with embodiments of the present invention. Raw datahistory (e.g., states stream) is passed from each local system 100A-100Eto the central unit located on a cloud system 710 and performs variousdata analysis to find correlation of states patterns among the multipleusers' data to identify new abnormal patterns that may be reflected justbefore the recognized abnormal pattern. New patterns code vectors willbe included to the CSA codebook and cloud remotely updates the multiplelocal systems with the new code-book. The data will be used to analyzedaily operation of local system 100A-100E.

FIG. 8 is a diagram illustrating a floor plan 800 of an exemplaryresidential environment (e.g., an apartment) on which the process forthe initial training is described herein. The home environment is mappedinto the primary zones (the major home places that the monitored personattends most of the time as bedroom 810, restroom 820, living room 830and the like) and secondary zones (the rest of the barely usedenvironments).

The VQ based human state classifier (described above) is trained to knowthe various primary places at the home. This is done during the systemsetup while the installer 10A (being the elderly person or anotherperson) stands or walks at each primary place such as bedroom 810,restroom 820, and living room 830 and let the system learns the “fingerprint” of the echo signals extracted features that mostly representsthat place. These finger prints are stored in the VQ positions codebook.

In addition, the system learns the home external walls boundaries. Thisis done during the system setup while the installer stands at variousplaces along the external walls and let the system tunes its power andprocessing again (integration) towards each direction. For example, inbedroom 810, installer 10A may walk along walls in route 840 so that theborders of bedroom 810 are detected by tracking the changes in the RFsignal reflections throughout the process of walking. A similar borderidentification process can be carried out in restroom 820, and livingroom 830.

Finally, the system learns to identify the monitored person 10B. This isdone by capturing the finger print of the extracted features on severalconditions, such as (1) while the person lays at the default bed 812(where he or she is supposed to be during nighttime) to learn theoverall body volume (2) while the person is standing to learn thestature, and (3) while the person walks to learn the gait.

All the captured cases are stored in the VQ unit and are used to weightthe pre-trained codebooks and to generate the specific home/personcodebooks.

According to some embodiments, one or additional persons such as 20 canalso be monitored simultaneously. The additional person can be anotherelderly person with specific fingerprint or it can be a care giver whoneeds not be monitored for abnormal postures.

FIG. 9 is a diagram illustrating yet another aspect in accordance withsome embodiments of the present invention. System 900 is similar to thesystem described above but it is further enhanced by the ability tointerface with at least one wearable medical sensor 910A or 910B coupledto the body of human 10 configured to sense vital signs of human 10, anda home safety sensor 920 configured to sense ambient conditions at saidspecified area, and wherein data from said at least one sensor are usedby said decision function for improving the decision whether an abnormalphysical event has occurred to the at least one human in said specifiedarea. The vital signs sensor may sense ECG, heart rate, blood pressure,respiratory system parameters and the like. Home safety sensors mayinclude temperature sensors, smoke detector, open door detectors and thelike. Date from all or some of these additional sensors may be used inorder to improve the decision making process described above.

In the above description, an embodiment is an example or implementationof the inventions. The various appearances of “one embodiment,” “anembodiment” or “some embodiments” do not necessarily all refer to thesame embodiments.

Although various features of the invention may be described in thecontext of a single embodiment, the features may also be providedseparately or in any suitable combination. Conversely, although theinvention may be described herein in the context of separate embodimentsfor clarity, the invention may also be implemented in a singleembodiment.

Reference in the specification to “some embodiments”, “an embodiment”,“one embodiment” or “other embodiments” means that a particular feature,structure, or characteristic described in connection with theembodiments is included in at least some embodiments, but notnecessarily all embodiments, of the inventions.

It is to be understood that the phraseology and terminology employedherein is not to be construed as limiting and are for descriptivepurpose only.

The principles and uses of the teachings of the present invention may bebetter understood with reference to the accompanying description,figures and examples.

It is to be understood that the details set forth herein do not construea limitation to an application of the invention.

Furthermore, it is to be understood that the invention can be carriedout or practiced in various ways and that the invention can beimplemented in embodiments other than the ones outlined in thedescription above.

It is to be understood that the terms “including”, “comprising”,“consisting” and grammatical variants thereof do not preclude theaddition of one or more components, features, steps, or integers orgroups thereof and that the terms are to be construed as specifyingcomponents, features, steps or integers.

If the specification or claims refer to “an additional” element, thatdoes not preclude there being more than one of the additional element.

It is to be understood that where the claims or specification refer to“a” or “an” element, such reference is not be construed that there isonly one of that element.

It is to be understood that where the specification states that acomponent, feature, structure, or characteristic “may”, “might”, “can”or “could” be included, that particular component, feature, structure,or characteristic is not required to be included.

Where applicable, although state diagrams, flow diagrams or both may beused to describe embodiments, the invention is not limited to thosediagrams or to the corresponding descriptions. For example, flow neednot move through each illustrated box or state, or in exactly the sameorder as illustrated and described.

Methods of the present invention may be implemented by performing orcompleting manually, automatically, or a combination thereof, selectedsteps or tasks.

The descriptions, examples, methods and materials presented in theclaims and the specification are not to be construed as limiting butrather as illustrative only.

Meanings of technical and scientific terms used herein are to becommonly understood as by one of ordinary skill in the art to which theinvention belongs, unless otherwise defined.

The present invention may be implemented in the testing or practice withmethods and materials equivalent or similar to those described herein.

While the invention has been described with respect to a limited numberof embodiments, these should not be construed as limitations on thescope of the invention, but rather as exemplifications of some of thepreferred embodiments. Other possible variations, modifications, andapplications are also within the scope of the invention. Accordingly,the scope of the invention should not be limited by what has thus farbeen described, but by the appended claims and their legal equivalents.

1. A non-wearable monitoring system comprising: a radio frequency (RF)interferometer configured to transmit signals at a specified area andreceive echo signals via an antenna array; an environmental cluttercancellation module configured to filter out static non-human relatedecho signals; a human state feature extractor configured to extract fromthe filtered echo signals, a quantified representation of positionpostures, movements, motions and breathing of at least one human locatedwithin the specified area; a human state classifier configured toidentify a most probable fit of human current state that represents anactual human instantaneous status; and an abnormality situation patternrecognition module configured to apply a pattern recognition baseddecision function to the identified states patterns and determinewhether an abnormal physical event has occurred to the at least onehuman in the specified area.
 2. The system according to claim 1, whereinthe extracted features are vector quantized, and wherein the classifieris configured to find the best match to a codebook which represents thestate being a set of human instantaneous condition/situation which isbased on said quantized features.
 3. The system according to claim 1,wherein the RF interferometer is an ultra-wide band (UWB) RFinterferometer which includes: a multiple synthetic aperture antennasarrays; UWB pulse generator, UWB transmitter and UWB receiver configuredto capture echo signal from every antenna and every array.
 4. The systemaccording to claim 1, wherein the environmental clutter cancellationmodule comprises a static environment detector configured to ensure thatno human body at the environment, static clutter estimator and staticclutter subtraction.
 5. The system according to claim 1, wherein thehuman state features extractor comprises: a back-projection unitconfigured to estimate the reflected clutter from a specific voxel toextract the human position and posture features; a Doppler estimatorconfigured to extract the human motions and breathing features; and afeatures vector generator configured to create a quantized vectors ofthe extracted features.
 6. The system according to claim 1, wherein thehuman state classifier comprises a training unit configured to quantizethe known states features vectors and generate the states code-vectors;a distance function configured to measure the distance between unknowntested features vectors and pre-defined known code-vectors; and aclassifier unit configured to find the best fit between unknown testedfeatures vector and pre-determined code-vectors set, classifier outputconsisting of the most probable state and the relative statisticaldistance to the tested features vector.
 7. The system according to claim1, wherein the abnormality pattern recognition module comprises: atraining unit configured to generate the set of abnormal states patternsas a reference codebook, and set of states transition probabilities, andstates patterns matching function to find and alert on a match between atested states pattern and the pre-defined abnormal pattern of thecodebook.
 8. The system according to claim 1, further comprising acommunication sub-system configured to communicate an alert upondetermining of an abnormal physical event.
 9. The system according toclaim 8, wherein the communication sub-system comprises: a signalinglink configured to transmit the system alerts to far end, a two wayvoice and video communication to interact with the monitored person, andtwo-way data link to remotely upgrade the system.
 10. The systemaccording to claim 8, further comprising a remote centralized dataanalyzing unit comprising: a data collection unit to receive themonitoring and alerts units' data, data analyzer to find new abnormalpatterns based on multiple users situations, abnormal patterns codebookgenerator to update the codebook with the new abnormal patterns.
 11. Thesystem according to claim 1, configured to operate with similar systemsin a master-slave configuration in which the RF interferometer and setof antennas are configured to act as a repeater communicating between atleast two systems.
 12. The system according to claim 1, whereinparameters obtained in a training sequence are used to configure thehuman state classifier per a specific person.
 13. The system accordingto claim 12, wherein the trained environmental parameters are calculatedby monitoring a human in various locations and positions in a predefinedorder.
 14. The system according to claim 7, wherein the abnormalpatterns reference codebook is remotely updated with new abnormalpatterns received from data analysis by multiple users.
 15. The systemaccording to claim 1, wherein the system further includes an interfacewith at least one of: a wearable medical sensor configured to sensevital signs of the human, and a home safety sensor configured to senseambient conditions at said specified area, and wherein data from said atleast one sensor are used by said decision function for improving thedecision whether an abnormal physical event has occurred to the at leastone human in said specified area.